We chose stacked ResBs to extract features from ECG data as they are easy to optimize and have previously been effective in generating discriminative features [1]}. The structure of the modified ResB we use is illustrated in Fig. REF . It addresses the optimization problem by introducing a deep residual learning framework. Instead of directly learning the underlying mapping \(H(\mathbf {x})\) , stacked layers in ResB approximate a residual function \(F(\mathbf {x})=H(\mathbf {x})-\mathbf {x}\) , where \(\mathbf {x}\)  is the input for ResB. Then the original function becomes \(H(\mathbf {x})=F(\mathbf {x})+\mathbf {x}\) .
This is easier to optimize as the identity mappings \(H(\mathbf {x})=\mathbf {x}\)  provides reasonable preconditioning.
